Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning.

Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques to assess hemorrhagic risk in patients with CAD. A total of 1638 patients with CAD were enrolled from January 2018 to October 2019, among which 114 patients were observed to have at least one positive event.

A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs.

. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease.

Single-channel EEG sleep staging based on data augmentation and cross-subject discrepancy alleviation.

Automatic sleep stage classification is an effective technology compared to conventional artificial visual inspection in the field of sleep staging. Numerous algorithms based on machine learning and deep learning on single-channel electroencephalogram (EEG) have been proposed in recent years, however, category imbalance and cross-subject discrepancy are still the main factors restricting the accuracy of existing methods. This study proposed an innovative end-to-end neural network to solve these problems, specifically, four data augmentation methods were designed to eliminate category imbalance, and domain adaptation modules were designed for the alignment of marginal distribution, conditional distribution, and channel and spatial level distribution of feature maps, as well as the capture of transferable regions on the feature maps using a transfer attention mechanism.

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

Objectives: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records.

Wavelet-based real-time calculation of multiple physiological parameters on an embedded platform.

Objective: This paper aims to present how physiological signals can be processed based on wavelet decomposition to calculate multiple physiological parameters in real-time on an embedded platform.

Approach: An ECG and PPG are decomposed to the appropriate scale based on a quadratic spline wavelet base in order to obtain high and narrow pulse peaks at the location of the mutation points. Based on the decomposed waveforms, feature points are positioned to calculate physiological parameters in real-time, including heart rate, pulse rate, blood oxygen, and blood pressure.

Predicting forced vital capacity (FVC) using support vector regression (SVR).

Objective: Spirometry, as the gold standard approach in the diagnosis of chronic obstructive pulmonary disease (COPD), has strict end of test (EOT) criteria (e.g. complete exhalation), which cannot be met by patients with compromised health states.

[Determination of critical micelle concentration of alkyl polyglucoside (APG) nonionic surfactant aqueous system by multi-peaks Gaussian fitting of visible absorption spectra line shape].

A multi-peaks Gaussian fitting on the line shape of visible spectra was used to determine the critical micelle concentration (CMC) of alkyl polyglucoside (APG) nonionic surfactant aqueous system such as octyl beta D mono-glucoside (C8 G1) and decyl beta D mono-glucoside (C10 G1). Visible electronic absorption spectra of a series of different concentration C8G1 or C10G1 with crystal violet (CV) used as a probe were measured respectively and characterized by the overlap of the principal peak with lambda(max) at 598-609 nm and a shoulder at 538-569 nm assigned to monomer and dimer CV respectively. A multi-peaks Gaussian fitting was used to interpret the spectra and give relative integrating absorbance (A2/A1) of two peaks, red-shift (deltalambda) and half-width.